HFT

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High Frequency Trading Framework with Machine/Deep Learning

In this project, we provide a framework/pipeline for high-frequency trading using machine learning and deep learning techniques. More advanced feature engineering (with depth, trade, and quote data) and models (such as pre-trained models) can be applied in this framework.

Target

• Extract trading signals from level-II order book data
• Predict order book dynamics using machine learning and deep learning techniques

Data

We use tick-level depth data of the SGX FTSE China A50 Index Futures (a major Asia-Pacific index future traded on the Singapore Exchange).

Strategy Pipeline

<img src="./Graph/pipline.png" width="650">

Order Book Signals

We use limit order book data to develop trading signals, including Depth Ratio, Rise Ratio, and Order Book Imbalance (OBI).

<img src="./Graph/depth.png" width="650">

Price Series

<img src="./images/best_bid_ask.png" width="750">

Feature Engineering & HFT Factors Design

• Simple average depth ratio and OBI:

<img src="./images/depth_0915_1130.png" width="750"> <img src="./images/depth_1300_1600.png" width="750">

• Weighted average depth ratio, OBI, and rise ratio:

<img src="./images/rise_1300_1600_w.png" width="750">

Model Fitting

• Basic Models:

  • RandomForestClassifier
  • ExtraTreesClassifier
  • AdaBoostClassifier
  • GradientBoostingClassifier
  • Support Vector Machines
  • Other classifiers: Softmax, KNN, MLP, LSTM, etc.

• Hyperparameters:

  • Training window: 30 min
  • Test window: 10 sec
  • Prediction label: 15 min forward

Performance Metrics

• Prediction accuracy:

<img src="./images/prediction.png" width="750">

• Prediction Accuracy Series:

<img src="./images/single_day_accuracy.png" width="800">

• Cross Validation Mean Accuracy:

<img src="./images/CV_result.png" width="800">

• Best Model:

<img src="./images/best_CV_result.png" width="800">

PnL Visualization

<img src="./images/best_CV_result_all.png" width="800">

Improvements

Feature Engineering

Other potentially useful signals:

• Volume imbalance signal
• Trade imbalance signal
• Technical indicators of bid and ask series (RSI, MACD, etc.)
• WAP/WPR, weighted average price, VWAP, TWAP
• ...

Signal generation techniques:

• Consider different weights for different levels of order book data for a particular signal
• Consider moving averages with period n (hyperparameter)
• Consider weighted averages of signals, such as weighted average of trade imbalance and order book imbalance
• Lasso regression, genetic programming
• ...

Models

This project only provides a baseline. More advanced models are welcome:

• CNN
• GRU/LSTM
• XGBoost, AdaBoost, GBDT, LightGBM
• Attention, Auto-encoder
• TabNet
• Pre-trained models
• ...

Performance Metrics

The performance metrics are subject to amendment, including the PnL calculation, commission fee consideration, etc.

👨‍💻 Author

Anurag Kumar Final-year student at IIIT Bhagalpur, India Passionate about quantitative finance, problem-solving, data structures and algorithms, and software engineering.

⭐ Support

If you like this project, consider giving it a ⭐ on GitHub — it really helps!

Also, feel free to follow me on GitHub for more projects and updates: 👉 https://github.com/anuragpy07